L05 - Soil and Water Spatial Variability

AGRI4401 Precision Agriculture

Gustavo Alckmin

June 30, 2025

Soil & Water Spatial Variability

  • Spatial vs temporal variability: definitions and implications
  • Soil texture: particle-size distribution affecting water-holding, nutrient retention, and root growth
  • Soil structure: aggregation patterns influencing aeration, water infiltration, and seasonal moisture dynamics
  • Mapping methods: electromagnetic induction (ECa) for texture proxies and gamma radiometrics for structural heterogeneity
  • Seasonal effects: how moisture fluctuation alters conductivity and radiometric signatures
  • Variability pattern analysis as basis for management zone delineation

Agenda

  • Precision Agriculture & Site-Specific Nutrient Management Overview
  • 4R Nutrient Stewardship Framework (Right Product, Rate, Place, Time)
  • Grid Soil Sampling & Spatial Data Acquisition
  • Geostatistical Interpolation Methods (Kriging, IDW, Cokriging)
  • Prescription Map Generation & GNSS-Guided Variable-Rate Application
  • Monitoring, PDCA Cycle & Feedback for Continuous Optimization

Soil Formation & Landscape Variability

  • Multi-scale soil and crop variability mapping: expert surveys, digital soil mapping, proximal/remote sensing, yield mapping
  • Strategic, tactical, operational decision levels for low- and high-tech systems
  • Management unit delineation based on pedogenic factors and landscape morphology
  • ICT-driven spatial integration linking soil heterogeneity to crop growth and input scheduling
  • Site-specific nutrient and water application to minimize leaching, runoff, and GHGs
  • Economic optimization through precision targeting of inputs to variable-rate zones

Key Formation Processes

graph TD
  A[Climate] --> F(Soil Formation)
  B[Organisms] --> F
  C[Relief] --> F
  D[Parent Material] --> F
  E[Time] --> F

  • Climate: chemical & physical weathering rates
  • Organisms: organic matter inputs & bioturbation
  • Relief: erosion, drainage, deposition
  • Parent material: mineralogy, texture, pH
  • Time: horizon differentiation & pedogenesis
  • CLORPT factors as spatial covariates

Weathering & Horizon Development

  • Physical weathering: freeze-thaw, abrasion
  • Chemical weathering: hydrolysis, oxidation
  • Biological contributions (root exudates)
  • Horizon formation: O, A, B, C

Overview of weathering processes and soil horizon formation.

graph TD
    A[Parent Material<br>Bedrock] --> B[Weathering<br>Physical, Chemical, Biological]
    B --> C[Additions<br>Organic matter]
    B --> D[Losses<br>Leaching]
    B --> E[Translocations<br>Clay, minerals]
    B --> F[Transformations<br>Humus formation]
    C --> G[Horizon Development]
    D --> G
    E --> G
    F --> G

Organic Matter Accumulation

  • Organic matter enhances soil structure & CEC
  • Humus content correlates with higher ECa
  • Organic-rich zones map distinctly on ECa surveys
  • Decomposition alters moisture retention & conductivity
  • Overlaps with anthropogenic management zones
  • ECa surveys delineate organic gradients for VRA

Landscape Controls on Variability

  • Topography: slope & aspect
  • Drainage patterns
  • Microclimates
  • Land-use history

Overview of how texture, structure, & topography drive spatial variability and management zoning.

flowchart LR
  A[Soil Sampling] --> B[Lab Analysis]
  B --> C[Variability Map]
  C --> D[Zone Delineation]
  D --> E[VRA]

Soil Physical Properties Overview

  • Apparent Conductivity (ECa): salt, clay, moisture, bulk density, OM, temperature
  • EMI vs electrode arrays for measurement
  • Spatial mapping: salinity, clay stratification, moisture, sand
  • Anthropogenic effects: leaching, drainage, compaction
  • ECa as proxy for water & nutrient zones
  • Correlation with yield for site-specific management

flowchart LR
  A[Soil Props] --> B(ECa Signal)
  C[Env Factors] --> B
  B --> D[Variability Maps]
  D --> E[Zones]
  E --> F[Recommendations]

Soil Texture

  • Sand/silt/clay percentages
  • Texture classes
  • Impacts on water retention & permeability

Illustration: ECa techniques for texture variability & zoning.

graph LR
  EM[EMI] --> Data[ECa Data]
  RES[Resistivity] --> Data
  Data --> Map[Georeferenced Map]
  Map --> Analysis[Variability]
  Analysis --> Zones[Management Zones]

Soil Structure

  • Macro (>250 µm), meso (53–250 µm), micro (<53 µm)
  • Aggregate stability & porosity metrics
  • Bulk density & compaction mapping
  • Impacts on root-zone water & aeration
  • Correlation with yield & moisture
  • Basis for VRA tillage & amendments

Soil Structure

graph TD
  A[Structure] --> B[Aggregate Classes]
  A --> C[Proximal Sensors]
  C --> D[Porosity Map]
  C --> E[Bulk Density]
  E --> F[Compaction Zones]
  F --> G[Root Constraints]
  G --> H[Yield Impacts]

Soil Porosity & Density

  • Porosity: void fraction controlling aeration, water, roots
  • Bulk density: mass per volume, inversely related to porosity
  • Core & gamma-ray methods for direct measurement
  • ECa sensitivity to density via dielectric coupling
  • Heterogeneity drives mapping resolution
  • Use for precision tillage & irrigation zoning

flowchart LR
  Porosity -->|inverse| BulkDensity
  BulkDensity --> SensorCoupling
  SensorCoupling --> ECa
  Porosity --> WaterRetention
  WaterRetention --> ECa

Spatial Variability of Physical Properties

  • Texture heterogeneity impacts water & nutrients
  • Aggregate stability & pore connectivity vary spatially
  • Bulk density & compaction hotspots alter roots & aeration
  • Infiltration & retention capacity create moisture gradients
  • Topographic gradients drive erosion & deposition
  • Geostatistical methods (variogram, kriging) define zones

Macropores & Micropores

  • Macropores (>0.08 mm): drainage, aeration
  • Micropores (<0.08 mm): water retention
  • Influence on root growth & water availability

graph TD
    A[Pore-Size Distribution in Soil]
    A --> B[Macropores >0.08 mm]
    A --> C[Micropores <0.08 mm]
    B --> D[Drainage]
    B --> E[Aeration]
    C --> F[Water Retention]
    D --> G[Prevents Waterlogging]
    E --> H[Facilitates Root Respiration]
    F --> I[Provides Water for Plant Uptake]
    G --> J[Promotes Root Growth]
    H --> J[Promotes Root Growth]
    I --> K[Enhances Water Availability]
    J --> L[Agricultural Productivity]
    K --> L[Agricultural Productivity]

Soil Compaction & Traffic Farming

  • Controlled Traffic Farming confines wheels to lanes, preventing subsoil compaction (>0.3 MPa @ 400 mm)
  • Preserves macroporosity & root penetration
  • Eliminates deep tillage energy costs
  • Improves infiltration, reduces runoff, erosion, GHGs
  • GNSS-guided implements ensure precise lane alignment
  • VRA within lanes optimizes input use

Soil Chemical Properties Overview

  • ECa integrates soluble salts, clay mineralogy, moisture, bulk density, organic matter, temperature
  • EMI & ERT surveys for rapid field-scale mapping
  • Spatial ECa patterns reveal salinity, clay lenses, moisture trends
  • Depth profiling differentiates shallow sand vs deep clay
  • Organic matter & management practices modulate signals
  • ECa as proxy for yield-limiting properties

Soil pH Variability

  • pH scale: acid → alkaline
  • Influences nutrient availability & microbial activity
  • Field-scale spatial patterns

graph LR
  A[EMI Survey] --> B[ECa Map]
  B --> C[pH Model]
  C --> D[Lime VRA Map]
  D --> E[Yield Benefit]

Soil Nutrient Content

  • Macronutrients: N, P, K
  • Secondary: Ca, Mg, S
  • Micronutrients: Fe, Mn, Zn
  • Spatial sampling strategies

graph LR
  A[EMI Sensor] --> B[ECa Data]
  B --> C[GIS Interpolation]
  C --> D[ECa Map]
  D --> E[Zones]
  E --> F[VRA]

Cation Exchange Capacity

  • CEC: capacity to retain/exchange Ca²⁺, Mg²⁺, K⁺, Na⁺
  • Units: cmol(+)/kg or meq/100 g
  • Governs nutrient buffering & leaching risk
  • Spatial mapping via grid sampling + cokriging
  • Informs 4R management: rate & placement
  • Enhances fertilizer efficiency

Soil Nitrate & Water Relations

  • Nitrate variability affects water-use efficiency & leaching risk
  • Temporal nitrate dynamics under variable irrigation
  • 7-R framework: adds form, feedback, resilience to 4Rs
  • Geo-tools enable nitrate VRA & irrigation control
  • Erosion controls reduce runoff
  • Riparian buffers & wetlands attenuate drainage nitrates

Role of Soil Mapping

  • Integrate surveys, digital mapping, remote sensing for zone delineation
  • Deploy proximal sensors + interpolation for high-res layers
  • Monitor moisture, nutrients, OM over time
  • VRA fertilization, tillage, irrigation recommendations
  • Strategic → tactical → operational decision hierarchy
  • Long-term soil health & environmental monitoring

Interpreting Soil Variability

  • GIS analysis of soil layers
  • Statistical summaries & thresholds
  • Management zone identification
  • Decision rules for inputs

flowchart LR
  EC[EC Sensor] --> IN[Integration]
  GR[Gamma Radiometry] -->
  HT[Hand Texturing] --> IN
  IN --> MAP[Variability Map]
  MAP --> ZONES[Management Zones]

Soil Mapping Technologies

  • Traditional expert surveys & farmer knowledge
  • Digital Soil Mapping: geostatistics + machine learning
  • In situ sensor networks: EC, moisture, pH, nutrients
  • Remote sensing: multispectral/hyperspectral, UAV
  • Crop sensing (NDVI, yield) for soil–crop dynamics
  • Integrated GIS management units

Creating a Basic Soil Map

graph LR
  A[Sampling Grid] --> B[GPS Georeferencing]
  B --> C[Variogram Analysis]
  C --> D[Spatial Interpolation]
  D --> E[Digital Map]
  E --> F[Validation]

  • Design grid by field size & zones
  • Georeference samples with high-precision GPS
  • Model variogram for spatial autocorrelation
  • Use kriging or IDW for surfaces
  • Integrate ancillary data (e.g., NDVI)
  • Validate via cross-validation & field checks

ECa Electrical Conductivity

  • ECa measures salinity & texture proxies
  • Methods: contact & non‐contact sensors
  • Applications: zoning, salinity mapping

Comparison of EM38 & Veris ECa depth responses and property correlations.

flowchart LR
  EM38[EM38 0-50 cm] --> DR[Depth Response]
  Veris[Veris 0-100 cm] --> DR
  DR --> Cor[Correlations]
  Cor --> Clay[Clay CEC]
  Cor --> Sec[OM, silt, moisture]

VERIS ECa (On-the-Go)

VERIS CoreScan

Soil Water Movement & Crop Growth

  • Infiltration driven by texture, structure, OM
  • Capillary vs gravitational flow in vadose zone
  • Root uptake via soil-root water potential gradients
  • VRA irrigation optimizes root-zone moisture
  • Conservation tillage & cover crops boost infiltration
  • Integrate water & nutrient management for yields & ecosystem

Field Capacity, Wilting Point, Saturation

graph TD
    A[Pore Size Distribution]
    A --> B[Macropores >0.08 mm]
    A --> C[Micropores <0.08 mm]
    B --> D[Drainage Saturation 0 kPa]
    B --> E[Aeration]
    C --> F[Water Retention Field Capacity -33 kPa]
    D --> G[Prevents Waterlogging]
    E --> H[Supports Root Respiration]
    F --> I[Water Available Until Wilting Point -1500 kPa]
    G --> J[Promotes Root Growth]
    H --> J[Promotes Root Growth]
    I --> K[Ensures Water Availability]
    J --> L[Agricultural Productivity]
    K --> L[Agricultural Productivity]

Field Capacity, Wilting Point, Saturation

  • Saturation: max θ ≈ porosity
  • Field Capacity: θ at ~−33 kPa after drainage
  • Wilting Point: θ at ~−1500 kPa, plant-unavailable
  • Moisture retention curve: θ vs ψ
  • Irrigation scheduling: maintain θ between FC & WP
  • Calibrate ECa–ECw–θ models

Root Growth Zone

  • Defines root depth & lateral spread
  • Influenced by compaction, moisture, nutrients
  • Drives water & nutrient uptake

flowchart LR
  SP[Soil Traits] --> NORM[Normalize]
  NDVI[NDVI Time Series] --> NORM
  NORM --> CL[Clustering]
  CL --> MAP[Root Zone Map]

Evapotranspiration Definition

  • ET = Evaporation + Transpiration flux to atmosphere
  • Drivers: net radiation, VPD, aerodynamic & canopy conductance
  • Units: mm·day⁻¹ or latent heat flux (W·m⁻²)
  • ETc = Kc · ETo (crop vs reference ET)
  • Potential vs actual ET (moisture-limited)
  • FAO-56 Penman–Monteith equation

Groundwater Movement Models

flowchart LR
  A[EM Survey] --> B[Regression Calibration]
  B --> C[Volumetric Water]
  C --> D[3D Moisture Surfaces]
  D --> E[Groundwater Model]
  E --> F[Yield Prediction]

  • Richards’ equation for unsaturated flow
  • Hydraulic parameterization via pedotransfer functions
  • Calibrate with dual-EM vs VWC (4 depths)
  • Generate 3D moisture & constraint surfaces
  • GIS integration for boundary conditions
  • Applications: water-use, risk management, yield analytics

MODFLOW Model Applications

flowchart LR
  A[Piezometers & Probes] --> B[Data Assimilation]
  C[Remote Sensing] --> B
  B --> D[MODFLOW Model]
  D --> E[Hydraulic Heads & Fluxes]
  E --> F[Crop Uptake Coupling]
  F --> G[Irrigation & Nutrient Decisions]

  • MODFLOW for 3D groundwater simulations
  • Data assimilation from piezometers, moisture probes, remote sensing
  • Inverse modeling calibrates conductivity & recharge
  • Coupled water uptake & solute transport models
  • Site-specific irrigation schedules from water-table dynamics
  • Case: maize irrigation efficiency improvement

Groundwater Pollution & Quality

  • Geospatial mappings & VRI to match inputs to crop demand
  • Grassed waterways, riparian buffers, wetlands intercept nutrients
  • Controlled drainage to limit preferential flow losses
  • Watershed-scale nutrient flow models for BMP optimization

Soil & Water Conservation Practices

  • Contour farming to reduce slope-driven runoff & erosion
  • Grassed waterways filter sediments & nutrients
  • Riparian buffers intercept flux & stabilize banks
  • Sediment ponds & wetlands sequester N & P
  • VRI applies water by moisture & ET data to minimize leaching
  • Integrate geospatial mapping with the 7Rs for precision conservation

Contour Farming & Terracing

  • Contour plowing follows elevation lines
  • Terraces reduce slope length & gradient
  • Effects: runoff velocity ↓, erosion ↓
Figure 1

Drainage Strategies & Salinity

  • Variable-rate subsurface drainage control
  • EM & moisture sensors for salinity mapping
  • Precision grassed waterways & contour drains
  • Controlled drainage & recycling systems
  • Watershed-scale salinity flux modeling
  • Remote sensing for saline hotspot monitoring

Surface Energy Balance Algorithm (SEBAL)

  • Estimates ET via surface energy balance
  • Derives Rn, G, H from satellite data
  • LE = Rn – G – H for latent heat flux
  • Uses albedo, NDVI, LST from multispectral imagery
  • Incorporates T, RH, wind for flux partitioning
  • High-res ET maps for irrigation & drought

SEBAL Applications

  • Field-scale ET for precision irrigation scheduling
  • Crop water productivity & yield forecasting
  • Regional water allocation & watershed management
  • Drought & stress detection across heterogeneous fields
  • Calibrate hydrological models with SEBAL fluxes
  • GIS integration for decision support

Practical: Create Soil Map

  • Collect georeferenced soil samples on a systematic grid
  • Analyze pH, OM, texture in lab or via proximal sensors
  • Interpolate with kriging in geostatistical software
  • Generate continuous raster layers of soil attributes
  • Cluster or threshold to define management zones
  • Export zones as shapefiles/GeoTIFFs for VRA systems

Practical: Management Plan

  • Soil map
  • Recommended variable-rate inputs
  • Irrigation zones

flowchart LR
  A[Profit-Driver Analysis] --> B[Field Data Collection]
  B --> C[Equipment Calibration]
  C --> D[Generate VRA Maps]
  D --> E[Implementation Schedule]
  E --> F[ROI Monitoring]

Practical: Apply Drainage Strategy

  • Multi-layer EM surveys to 1.6 m depth
  • Calibrate EM vs volumetric water (cores & neutron probes)
  • Interpolate 3D surfaces of water-holding capacity & constraints
  • Apply pedotransfer functions in dry zones
  • Install neutron tubes in high/mean/low EM zones
  • Integrate datasets for drainage recommendations & yield forecasts

Key Takeaways

  • Integrated sensor networks for mass-flow & loss monitoring
  • Systematic protocols aligned with GRDC industry benchmarks (1–2% loss)
  • Manufacturer-recommended machine settings optimized per crop & moisture
  • Grain loss reduction from >2.5% to 1–2% → $37,460 profit gain on 1,000 ha barley
  • Throughput ↑ and power ↓ via targeted header & cleaning setups
  • Continuous data-driven calibration ensures sustained efficiency

Next Steps

  • Finalize farm-specific profit driver assessment
  • Map PA profit pathways for yield & input optimization
  • Apply five diagnostic questions across rainfall zones
  • Validate feasibility, data needs, and ROI timelines
  • Review PA technical sources (trials, sensor specs, models)
  • Consult PA glossary & advisors for implementation planning